Forecasting GDP growth based on Ant Colony Clustering Algorithm and RBF neural network
Jianna Zhao, Xinying Wang, Zhuozheng Wu · 2008
In order to forecast GDP growth much more accurate, a hybrid intelligent system is applied to improve the precision of forecasting, which combines ant colony clustering algorithm (ACCA) and RBF neural network. At first, we can make use of ACCA to cluster the data. And then, this clustered data is used to develop classification rules and train RBF neural network. The effectiveness of our methodology was verified by experiment data.